Gemma 4 vs GLM-5.1: Two Open-Source Heavyweights Compared

The open-source AI landscape in 2026 is defined by two very different philosophies. On one side, Google DeepMind's Gemma 4 — a family of compact, multimodal models designed to run everywhere from data centres to your phone. On the other, Zhipu AI's GLM-5.1 — a 754-billion-parameter Mixture-of-Experts beast that just topped SWE-Bench Pro.

Both are open-weight. Both claim frontier-level performance. But they're built for fundamentally different things. Here's the breakdown.

The Models at a Glance

The size difference alone tells the story. Gemma 4's largest model is 31B dense — something you can run on a single high-end GPU. GLM-5.1 is 744B total parameters, requiring serious infrastructure just to load.

Architecture: Compact Efficiency vs Massive Capacity

Gemma 4 introduces several clever architectural choices that maximise quality per parameter:

• Per-Layer Embeddings (PLE): A parallel embedding pathway that gives each transformer layer its own channel for token-specific information, instead of cramming everything into a single input embedding. This is especially impactful for the smaller E2B and E4B models.
• Shared KV Cache: Later layers reuse key-value states from earlier layers, cutting memory and compute for long-context generation with minimal quality loss.
• Dual RoPE: Standard RoPE for sliding-window attention layers, proportional RoPE for global layers — enabling longer contexts without the quadratic cost.
• Variable image encoding: The vision encoder preserves original aspect ratios and lets you trade off between 70 and 1,120 tokens per image, finding your own speed/quality sweet spot.

The result: Gemma 4 31B achieves an estimated LMArena score of 1452 (text only), and the 26B MoE hits 1441 with just 4B active parameters. That's frontier-adjacent quality at a fraction of the compute.

GLM-5.1 takes the opposite approach. Its 256-expert MoE architecture activates 40-44B parameters per token from a 744B total pool. Trained on 28.5 trillion tokens across 100,000 Huawei Ascend 910B chips — no Nvidia hardware involved — it's a statement about infrastructure independence as much as model capability.

GLM-5.1's differentiator isn't architectural novelty. It's scale and post-training. The 28% coding improvement from GLM-5 to 5.1 came through post-training refinement, not architecture changes. And the model is optimised for long-horizon agentic tasks — it can work autonomously on a single task for up to 8 hours.

Benchmarks: Where Each Model Wins

This is where it gets interesting. These models dominate different domains.

GLM-5.1's Territory: Coding and Agentic Tasks

GLM-5.1 currently holds the SWE-Bench Pro crown at 58.4% — edging out GPT-5.4 (57.7%) and significantly beating Claude Opus 4.6 (54.2%). For software engineering agents, this is the model to beat.

Gemma 4's Territory: Reasoning and Efficiency

For context, GLM-5 scores 95.4% on AIME 2026 and 86.0% on GPQA-Diamond — higher on raw reasoning numbers. But Gemma 4's scores are remarkable for models under 31B parameters, and they come at a fraction of the inference cost.

The smaller Gemma 4 variants punch well above their weight too:

Multimodal: Gemma 4 Wins by Default

This isn't close. Gemma 4 is natively multimodal — text, images, video, and (for E2B/E4B) audio. GLM-5.1 is text-only.

Gemma 4's multimodal capabilities are real and practical:

• Object detection and pointing: Outputs bounding boxes in JSON natively, no special prompting needed
• OCR and document understanding: Handles complex layouts and handwriting
• Video understanding: Processes video frames with audio (E2B/E4B) or without (31B/26B)
• Multimodal function calling: Can reason over images and trigger tool calls simultaneously

If your use case involves images, video, or audio, GLM-5.1 simply can't compete.

Cost and Deployment

Gemma 4 is designed to run everywhere:

• The 31B model runs on a single A100 or H100
• The 26B MoE (4B active) runs on consumer GPUs with quantisation
• The E2B and E4B run on phones and edge devices
• Supported by transformers, llama.cpp, MLX, WebGPU, Mistral.rs, and more

GLM-5.1 is a different beast:

• Self-hosting requires ~1.49TB storage and serious GPU infrastructure
• API pricing: $1.00 / $3.20 per million tokens (input/output)
• Compared to Claude Opus 4.6 at $15.00 / $75.00 — roughly 20-25x cheaper
• Most teams will use it via API rather than self-hosting

The Geopolitical Angle

GLM-5.1's training on 100,000 Huawei Ascend 910B chips is quietly significant. It's proof that frontier-adjacent AI can be trained without Nvidia hardware — a claim many in the industry doubted. For organisations concerned about US export controls or seeking supply chain diversification, this matters as much as any benchmark number.

Gemma 4, trained on Google's TPU infrastructure, represents the more established path. Both models are open-weight, but their training provenance tells different stories about the future of AI infrastructure.

Which One Should You Pick?

Pick Gemma 4 if:

• You need multimodal capabilities (images, video, audio)
• You want to run models locally or on-device
• You need a model that runs on consumer hardware
• Your use case involves vision, OCR, or document processing
• You want the most capable model per dollar of compute

Pick GLM-5.1 if:

• You're building coding or software engineering agents
• You need state-of-the-art SWE-Bench Pro performance
• Your tasks are long-horizon and agentic (8+ hours of sustained work)
• You need cybersecurity or NL2Repo capabilities
• Cost-per-token matters more than local deployment
• You want open weights under MIT license for customisation

The honest answer for many teams is: use both. Gemma 4 for vision, edge, and on-device tasks. GLM-5.1 for coding agents and complex software engineering. They're complementary, not competing — different tools for different jobs.

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